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1. Field of the Invention
This present invention relates generally to a method and apparatus for controlling physiological parameters and more particularly to an optimal controller for controlling glucose levels in a patient.
2. Description of the Related Art
Different types of sensors (e.g., optical sensors) are available for monitoring of physiological parameters (e.g., glucose concentration). Glucose monitoring is typically performed by people with diabetes mellitus which is a medical condition involving a body""s inability to produce the quantity or quality of insulin needed to maintain a normal circulating blood glucose. Frequent monitoring of glucose is generally necessary to provide effective treatment and to prevent long term complications of diabetes (e.g., blindness, kidney failure, heart failure, etc.). New methods of monitoring glucose are fast, painless and convenient alternatives to the typical capillary blood glucose (CBG) measurements which involve finger pricks that are painful, inconvenient and difficult to perform for long term.
Optical measurement of glucose is performed by focusing a beam of light onto the body. Optical sensors determine glucose concentration by analyzing optical signal changes in wavelength, polarization or intensity of light. However, many factors other than glucose concentration also contribute to the optical signal changes. For example, sensor characteristics (e.g., aging), environmental variations (e.g., changes in temperature, humidity, skin hydration, pH, etc.), and physiological variations (e.g., changes in tissue fluid due to activity, diet, medication or hormone fluctuations) affect sensor measurements.
Various methods are used to improve the accuracy of the sensor measurements. One method (e.g., multivariate spectral analysis) utilizes calibration models developed by initially measuring known glucose concentrations to correct subsequent sensor measurements. The calibration models become inaccurate over time due to dynamic changes in physiological processes. Another method (e.g., adaptive noise canceling) utilizes signal processing to cancel portions of the sensor measurements unrelated to glucose concentration. For example, two substantially simultaneous sensor measurements at different wavelengths make up a composite signal which can be processed to cancel its unknown and erratic portions. However, many sensors do not provide substantially simultaneous measurements at two different wavelengths.
The present invention solves these and other problems by providing a method and apparatus for making optimal estimates of a physiological parameter (e.g., glucose level), assessing reliability of the optimal estimates, and/or providing optimal control of the physiological parameter in real time using one or more sensor measurements at each measurement time epoch (or interval). The sensor measurements can be time-based (e.g., every five minutes) to provide continuous monitoring and/or regulation of the physiological parameter. The sensor measurements are a function of the physiological parameter within specified uncertainties.
An optimal estimator provides an accurate estimate of glucose level in real time using a sensor with at least one output. In one embodiment, the optimal estimator is integrated with the sensor and an output display to be a compact glucose monitoring device which can be worn by a patient for continuous monitoring and real-time display of glucose level. In an alternate embodiment, the optimal estimator is a separate unit which can interface with different types of sensors and provide one or more outputs for display, further processing by another device, or storage on a memory device.
In one embodiment, the optimal estimator employs a priori deterministic dynamic models developed with stochastic variables and uncertain parameters to make estimates of glucose level. For example, glucose level is defined as one of the stochastic (or random) variables. Dynamic mathematical models define process propagation (i.e., how physiological and sensor parameters change in time) and measurement relationship (i.e., how physiological and sensor parameters relate to environmental conditions). Environmental conditions (e.g., temperature, humidity, pH, patient activity, etc.) can be provided to the optimal estimator intermittently or periodically via environment sensors and/or data entries by a patient or a doctor.
The optimal estimator uses dynamic models to propagate estimates of respective stochastic variables, error variances, and error covariances forward in time. At each measurement time epoch, the optimal estimator generates real-time estimates of the stochastic variables using one or more sensor outputs and any ancillary input related to environmental conditions. In one embodiment, the optimal estimator employs a linearized Kalman filter to perform optimal estimation of the stochastic variables (e.g., glucose level). In particular, an extended Kalman filter is used to accommodate nonlinear stochastic models.
Before making real-time estimates, the optimal estimator is initialized by providing initial values for the stochastic variables, error variances, and error covariances. For example, a CBG measurement or another direct glucose measurement is performed at initialization to provide a starting value for the stochastic variable corresponding to glucose level.
In one embodiment, the optimal estimator provides one or more outputs to a patient health monitor which is capable of optimized real-time decisions and displays. The patient health monitor evaluates system performance by assessing the performance of the sensor and/or optimal estimator in real time. For example, the patient health monitor applies statistical testing to determine the reliability of the real-time estimates of the stochastic variables by the optimal estimator. The statistical testing is performed in real time on residual errors of the optimal estimator to establish performance measures.
In one embodiment, the patient health monitor acts as an input/output interface between the patient or medical staff (e.g., a doctor, nurse, or other healthcare provider) and the optimal estimator. For example, environmental conditions can be provided to the patient health monitor for forwarding to the optimal estimator. Optimal estimator outputs can be provided to the patient health monitor for display or forwarding to an external device (e.g., a computer or a data storage device).
In one embodiment, the optimal estimator provides one or more outputs to an optimal controller which can regulate in real time the physiological parameter being monitored. For example, an optimal controller responds to real-time optimal estimator outputs and provides an output to operate an actuator. In the case of glucose control, the actuator can be a dispenser or a pump which secretes insulin to correct a relatively high glucose level and glucagon to correct a relatively low glucose level. The optimal controller takes advantage of a priori information regarding the statistical characteristics of the actuator and is able to control the output of the actuator to be within specified uncertainties.
In one embodiment, the optimal estimator and the optimal controller form an optimal closed-loop system. For example, a glucose sensor, an optimal estimator, an optimal controller, and an insulin/glucagon dispenser work together as an artificial pancreas to continuously regulate glucose level. The glucose sensor can be internal or external to a patient""s body. The optimal controller provides a control feedback to the optimal estimator to account for delivery of the insulin/glucagon.
The optimal closed-loop system is effective in a variety of biomedical applications. For example, cardiovascular functions can be continuously regulated by using sensors to detect blood pressure, blood oxygen level, physical activity and the like, an optimal estimator to process the sensor measurements and make real-time estimates of heart function parameters, and an optimal controller to control operations of an artificial device (e.g., a pacemaker) in real time based on the real-time estimates from the optimal estimator to achieve a set of desired heart function parameter values. Other artificial devices (e.g., artificial limbs, bionic ears, and bionic eyes) can be part of similar optimal closed-loop systems with sensors detecting nerve signals or other appropriate signals.
The optimal closed-loop system is also effective in optimal treatment of chronic illnesses (e.g., HIV). Some medications for treatment of chronic illnesses are relatively toxic to the body. Over delivery of medication generally has adverse effects on the patient. The optimal closed-loop system is capable of providing effective and safe treatment for the patient. For example, an optimal estimator provides real-time estimates of key physiological parameters using one or more sensors, and an optimal controller controls a slow infusion of medication in real time based on the real-time estimates from the optimal estimator to obtain desirable values for the key physiological parameters.
In one embodiment, the optimal estimator, patient health monitor, and optimal controller are software algorithms which can be implemented using respective microprocessors. New information regarding process propagation or measurement relationship can be easily incorporated by modifying, reconfiguring, and/or adding to the software algorithms. The optimal estimator, patient health monitor, and optimal controller can be implemented as one joint algorithm or separate respective algorithms which function together to provide an optimal closed-loop system.